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Helping the Poor Manage Natural Disaster Risks

Dr. Jerry Skees H.B. Price Professor, University of Kentucky and President, GlobalAgRisk, Inc.

Cornell University CIIFAD seminar October 20, 2010

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GlobalAgRisk, Inc.

  Activities   Research and development   Technical capacity building   Educational outreach

  Supported by   Multinational donors   Governments   Nongovernment organizations

  Mission Improve access to financial services for the rural poor through innovative approaches for transferring natural disaster risk

  Select Country Work   Peru – El Niño/Flood   Mongolia – Livestock   Vietnam – Flood/Drought   Mali – Drought   Morocco – Drought   Mexico – Drought   Romania – Drought   Ethiopia – Drought   Next… Indonesia to bundle savings

and earthquake insurance

Natural Disasters and Developing Countries   Risks for losses caused by climate-related natural hazards are

rising   95 per cent of deaths from natural disasters in the last 25 years

occurred in developing countries

  Currently US$100 billion economic losses / annum

  Limited allocation of financial resources so far to support adaptation

  Developing countries

  Have lowest coping capacity, higher vulnerability (majority of fatalities)

  Types of Major Natural Disasters   Droughts

  Floods

  Earthquakes 3

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Rural Poverty

  More than 1 billion people currently live on less than one US dollar per day.

  Approximately 75 percent of those live in rural areas.

  The agricultural sector employs 40 percent of lower income countries workers

  Agricultural workers contribute over 20 percent of the GDP in over 60 lower income countries

  In many rural areas the risk of extreme weather events contributes to poverty and low rates of economic growth. Large numbers of working poor

Somebody Pays for Catastrophic Weather Risk

  The poor pay (ex post and ex ante)

  Financial institutions and firms in the value chain restrict services as they learn that the correlated losses of many of their borrowers and savers create significant problems in repaying

  Governments seek solutions — Disaster assistance, infrastructure investments, subsidized agricultural insurance

  Donors forgive debt and provide funds for recovery

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Extreme Weather Events and Poverty   Extreme weather events (e.g., drought or flood):

 Directly destroy assets (obtained at very high opportunity cost in foregone consumption);

 Cause households to liquidate assets (often when prices are very low); and/or

 Cause households to reduce investment in assets (e.g., health or education).

  The diminished asset base reduces future economic growth.

Extreme Weather Events and Poverty   Risk-averse households recognize their exposure to extreme

weather events and thus, adopt low-risk, low-return strategies for using productive assets.

  Because many extreme weather events are spatially covariate, lenders and other “risk aggregators” become reluctant to provide valuable services to rural households.

  Implications: low rates of economic growth, hopelessness, poverty traps.

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Informal Risk Management and Risk Coping   Rural households manage risk informally by:

 Mitigation (e.g., diversification of income generating activities, investments in improved crop inputs, share tenancy);

  Self-insuring (e.g., savings, credit reserves, liquidating physical assets);

 Risk-transfer (reciprocity obligations within families or local religious communities).

  Parents take children out of schools and put them to work

  These mechanisms also tend to fail when extreme covariate weather events occur.

What is Index Insurance ?

Pays for losses based on an independent and objective third party measure that is highly correlated with losses o  Area county yields (U.S. Group Risk Plan) o  Rainfall deficit using weather station o  Excess flooding using river level o  Extreme sea surface temperatures (Peru) o  Area mortality rates for Mongolia o  NDVI measures for poor pasture conditions in Kenya o  Richter scale measures for earthquakes o  Wind speed for hurricane damage

Challenges with Weather Index Insurance   Data limitations are perhaps the greatest challenge.

 Data for determining relationship between potential indexes and realized losses.

 Data for pricing the insurance and triggering indemnities.

  Other Challenges   Legal and Regulatory  Developing expertise of local insurance suppliers.  Creating an understanding of insurance products.  Obtaining reinsurance.

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What’s makes this interesting ? “This I believe”

  Advances in technology to measure events that create catastrophic losses

  Financial innovations for transfer of natural disaster risk   Improved thinking about legal and regulatory issues for

implementing index insurance in developing countries   Growing recognition that transfer of natural disaster risk our

of the country is critically important for social safety net, government infrastructure, firms that serve the working poor, and for ultimately for small households

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Consequential Losses from Natural Disaster Data Come via Risk Assessment   Ethiopia , Morocco, Kenya, etc.

  Mongolia — Livestock mortality

  Early flooding in the Mekong Delta

  Drought for coffee growers in Vietnam

  El Niño catastrophic flooding

  Livelihoods insurance for small households?

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Weather Index Insurance — Experience, Promise, and Challenges

  Weather index insurance is about ex ante financing and improving the way stakeholders pay for catastrophic losses (Can include market and government solutions)

  Addresses many of the challenges associated with providing financial services in rural areas

  More critical thinking and research are needed to integrate these products into the financial sector and to create long-term sustainable products that remain after donor interest has waned

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Insurance is expensive ̶ Focus must be catastrophic risk

Financial Services and Correlated Weather Risk

Financial services are complementary — A blend of savings, credit, and insurance is likely most effective for risk management

  Savings and credit best for small to moderate losses

  Insurance is best for catastrophic losses

0 500 1000 1500 2000 2500 3000 3500

Frequent, Less Severe Risk; Independent Losses

Savings

Less Frequent, Moderate Risk

Credit

Correlated Losses from Excess Rainfall Insurance

Economies with banking and insurance markets grow faster than those with only banking services

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Insurance is not for all segments   Insurance is not an efficient or effective mechanism to

transfer income Must be more careful about separating the social and the commercial aspects of index insurance

  The poorest of the poor need carefully developed safety net programs – cash transfers. These can use ‘insurance-like’ designs to reduce fraud and corruption.

  The working poor need access to effective insurance products: either directly or indirectly via the firms that they do business with

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Market Development Process Important — to Reach Scale Quickly

  To reach scale quickly, target risk aggregators first

  Introducing products with potential for significant scale engages the interest of key stakeholders (e.g., insurer, insurance regulator, global reinsurer) to provide input and services that are appropriate for longer-term sustainability

  Pilot projects that sell a few hundred policies to small households (small insured value) are often viewed as experimental and are less likely to receive the same attention

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Mongolia — Index-based Livestock Insurance

The Risk Severe livestock losses due to dzud (harsh winter weather)

Target Users Herders

Contract Structure Payments based on livestock mortality rates at the soum (county) level

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Index-based Livestock Insurance — Risk Layering A New Model for Public-Private Partnerships

GCC — Social Insurance

A layer of very infrequent risk where decision makers may have a cognitive failure problem

LRI — Commercial Insurance

Offered by private companies with reinsurance from government and now a global reinsurer

Government Catastrophe Cover (GCC)

Paid by government using a World Bank

contingent loan

Livestock Risk Insurance (LRI)

Retained by Herders

 6%  mortality  

   30%  mortality  

100%  mortality  

If the government can’t continue to pay for extreme losses, the

commercial layer can continue

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Mongolia Experience after 5 Years of Sales

  In 2006, the first sales season, 2400 policies were sold in 3 aimags (provinces). In 2010, nearly 7,000 policies were sold in 9 aimags

  Participation has grown from under 8% of eligible herders to about 20% in the original pilot aimags

  Herders paid an average premium of USD 50 in 2009 and received an average USD 326 in 2010

  Insurance companies understand and support the pooling arrangement and regulations

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El Niño Insurance for Flood Innovation in Northern Peru

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Extreme Flooding and El Niño

  Extreme flooding in Piura is directly tied to El Niño

  Warm Pacific trade winds meet cold air coming down Andes Mountains

  Result — Extreme, prolonged rainfall

  Severe El Niño occurs roughly 1 in 15 years

  Most recent severe El Niño events: 1982/83 and 1997/98

  Rainfall was 40x normal from January to April

  For 1997/98, volume of Piura River was 41x median value

  For 1982/83, volume of Piura River was 36x median value

  El Niño is the biggest risk event for agriculture, also affects many other sectors due to infrastructure breakdowns

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Extreme El Niño Events of 1982/83 : 1997/98

1997/98 1998 1983

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El Niño Southern Oscillation (ENSO) Index (Nov–Dec) 1979–2008

1983 Payment Rate = 34%; 1998 Payment Rate = 71% Start Threshold = 24.5; Exit Threshold = 27 31

ENSO Region 1.2

  Measured and reported by the NOAA Climate Prediction Center for over 50 years

  Coordinates   (0°-5°S, 90°W-80°W and 5°S-10°S, 90°W-80°W)

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Contract Payout Structure First Regulated Insurance to Pay Based on Forecast

Exit Trigger

Start Trigger

Linear payout so that if sea surface temperature is halfway between 24.5 and 27, or 25.75, the payout rate is 50%

Exit Trigger 27.0

Start Trigger 24.5

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Target Market: Commercial and Social

 Financial institutions  Firms in value chain  Fisheries  Farm Groups  Transportation  Tourism  Health Sector  Civil defense  Infrastructure

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Natural Disaster Effects on MFIs   Loan portfolio — Systemic repayment problems for borrowers,

problems can remain for years

  Deposits — Depositors withdraw funds

  Costs increase — Costs of funds (e.g., Interbank loans), administrative costs

  Resulting problems

  Liquidity

  Profitability

  Capital Adequacy

  Lending institutions have many ways of managing these risks (e.g., Provisions, restructuring loans, etc.)

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Risk Assessment Includes Evaluating Current Risk Management Strategies

  Potential strategies for managing these risks and their costs   Liquidity Hold higher portion of assets in cash

  Effect — Reduces investment in productive assets

  Profitability Avoid exposed regions and sectors   Effect — Limits growth opportunities, especially for untapped

markets

  Capital adequacy Leverage a lower amount of equity to provide a “cushion” for the risk   Effect — Limits growth

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,Equity/Net Credits Approximation for Capital Adequacy

El Niño

  Bank is localized in an exposed area   Insurance payout enters balance sheet as an asset Increases equity   Prevents cyclical nature in lending   Also allows bank to lend heavily in rebuilding effort after the disaster Produced by Benjamin Collier

Sum insured — 5% of loan portfolio’s value

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15 year ending net worth Monte Carlo Simulation, 1000 trials

No  Seguros    Seguros    Promedio   442     476    

Desviación  estándar     256   114  

Sum insured is 5% of credit portfolio

Produced by Benjamin Collier

Market Development Process Recommendations   Focus on legal and regulatory issues from the beginning

  Enabling legal/regulatory environment supports market sustainability and growth

  Use subsidy only for start-up costs and market-failures instead of premiums – be clear about social versus commercial approaches   Premium conveys important signals that should not be distorted

  Start up activities (risk assessment, education, training) provide a public good and can stimulate other markets

  Include plans for impact assessment from the start   Scalable and sustainable do not ensure poverty reduction

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Conclusions and the Way Forward Focus on

  Index insurance to insure against low-probability, catastrophic risks

  Can minimize basis risk and provide protection against a layer of risk that is most difficult to manage through other means (e.g. savings, credit, traditional indemnity insurance)

  Insuring against the broader consequential losses that result from a weather event

  Minimizes basis risk and should increase the value of the product to the target market in comparison to index insurance that is designed only around the vulnerabilities of a specific crop

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Conclusions and the Way Forward (Continued)

  Targeting new index insurance markets towards risk aggregators   Can reduce data requirements and capacity building needs and is likely

the only feasible means of extending weather index insurance products into many regions of the world, for the near future

  Rural lenders   Value Chain   Farmer Associations

  Carefully move to micro products with concept of livelihoods insurance for consequential losses suffered by small households; challenges will remain for demand and delivery

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